Genetic motif discovery applied to audio analysis

Motif discovery algorithms are used in bioinformatics to find relevant patterns in genetic sequences. In this paper, the application of such methods to audio analysis is proposed. In the presented system, sounds are first transformed into a sequence of discrete states, corresponding to characteristic spectral shapes. The resulting sequences are then subjected to the MEME algorithm for motif discovery, which estimates a structured statistical model for each found motif. The system is evaluated in two tasks: the discovery of repetitive patterns in a large sound database, and the detection of specific audio events in an audio stream. Both tasks are unsupervised and demonstrate the viability of the approach.